the supply and demand for licensed practical nurses and ... · registered nurses and licensed...

The Supply and Demand for

Registered Nurses and Licensed Practical Nurses

in Nebraska

February 6, 2006

David I. Rosenbaum, Ph.D.

4103 South Gate Blvd

Lincoln NE 68506

402-489-1218

[email protected]

David I. Rosenbaum, Ph.D. 402-489-1218

Economic Consulting [email protected] i

Executive Summary

Recent trends suggest that the expected demand for registered nurses (RN) and licensed

practical nurses (LPN) may exceed the supply in coming years. This report examines the

potential divergence in Nebraska.

National Center for Health Workforce Analysis (NCHWA) models are used to project

demand and supply for full-time equivalent registered nurses (FTE RN) in Nebraska from

2006 through 2020. Results show that demand will grow from a current need for about

16,000 full-time equivalent nurses to more than 20,000 over the next 15 years. However,

supply is projected to increase from about 15,000 registered nurses in 2006 to only

16,500 by 2020. Hence, a significant shortage will develop.

Analysis shows that several policies may help to reduce the RN shortage. Policies to

stem net out-migration, delay retirements, reduce career changes, promote full time

employment, and attract new RNs will all increase the supply of nurses.

The most effective long-term policies are those that continually attract people into

nursing. The advantage of such policies is that they add new nurses year after year.

Thus, while the increase in any year may be modest, the cumulative effect can be

significant. Reducing net annual out-migration and attracting more young people to

nursing as a profession will have these cumulative effects.

Policies that just affect participation among the existing pool of nurses will have more

limited long-term impacts. However, they will increase the supply of nurses in the short

term. Delaying retirements, reducing career changes and increasing the percentage of

nurses working full time as opposed to part time are examples of such policies.

The analysis also shows that no individual policy is likely to eradicate the nursing

shortage by itself. Rather, eliminating the nursing shortage will most likely require

participants developing a variety of policies to stimulate nursing supply. The most

effective solution may be one that stimulates supply among existing nurses to stem the

current shortage and simultaneously encourages more people into nursing to stem future

shortages.

The NCHWA model is used to project LPN demand as well. Unfortunately, there is no

model to project LPN supply. Alternative methods are used to estimate the supply of

LPNs between 2006 and 2020. As might be expected, there will be a shortage of LPNs

each year, growing from eight percent of demand in 2006 to almost 23 percent of demand

by 2020. It can reasonably be assumed that the same policies that will eliminate RN

shortages may reduce or eliminate the expected LPN shortages as well.


Economic Consulting [email protected] ii

Table of Contents

Page

Executive Summary ...................................................................................... i

Table of Contents ......................................................................................... ii

List of Tables ............................................................................................... iv

I. Introduction...................................................................................................1

II. The Nursing Demand Model........................................................................1

Number of People Requiring Care ..........................................................1

FTE RNs per Capita ................................................................................3

FTE Nursing Demand .............................................................................5

Making the Model Nebraska Specific .....................................................5

Population........................................................................................5

RN Usage .........................................................................................5

Results.......................................................................................................7

III. The Nursing Supply Model ..........................................................................7

RN Population..........................................................................................8

Starting Population Pool .................................................................8

Migration .........................................................................................8

Education .........................................................................................8

Attrition ............................................................................................8

Foreign Immigration........................................................................8

New Graduates.................................................................................8

RN Participation ......................................................................................9

RN FTE Equivalents................................................................................9

FTE Nursing Supply ................................................................................9

Making the Model Nebraska Specific .....................................................9

Initial Number of Nurses..................................................................9

Initial Number of Graduates ..........................................................10

Results.....................................................................................................11

IV. Comparing RN Supply and Demand ........................................................12

V. Policies to Impact Nursing Shortages .......................................................12

Factors Influencing Demand ................................................................12

Factors Influencing Supply ...................................................................12

Migration .......................................................................................12

Education .......................................................................................13

Attrition ..........................................................................................14

New Graduates...............................................................................17

FTE Rate ........................................................................................18

Cumulative Policies .......................................................................19


Economic Consulting [email protected] iii

VI. The LPN Demand Model ...........................................................................19

Number of People Requiring Care ........................................................19

FTE LPNs per Capita ............................................................................20

Results.....................................................................................................22

VII. LPN Supply..................................................................................................23

VIII. Comparing LPN Supply and Demand ......................................................25

IX. Recommendations .......................................................................................26


Economic Consulting [email protected] iv

List of Tables

Table Page

1 Factors Affecting Numbers of Patients in Health Care Settings.....................2

2 RN Staffing Intensity by Health Care Setting.................................................3

3 Factors Affecting RN Staffing Intensity in Health Care Settings...................4

4 Adjusting the NDM to Reflect Nebraska FTE RNs in 2002 ..........................6

5 NDM Estimated Nebraska Demand for FTE RNs..........................................7

6 Number of Nurse Graduates from Nebraska Colleges and Universities ......10

7 NSM Estimated Nebraska Supply of FTE RNs............................................11

8 Estimated Surplus/Shortage of FTE RNs .....................................................12

9 Decreasing Emigration Rate and Increasing Immigration Rate –

Estimated Shortage of FTE RNs................................................................13

10 Attrition Rates for RNs by Age ....................................................................14

11 Smoothing Attrition Rates Among Nurses Ages 62-64 – Estimated

Shortage of FTE RNs.................................................................................15

12 Reducing Attrition Rates Among Nurses Ages 62-65 – Estimated

Shortages of FTE RNs ...............................................................................16

13 Increasing Nursing School Graduates – Estimated Shortages of

FTE RNs ....................................................................................................17

14 Increasing FTE Rates – Estimated Shortages of FTE RNs...........................18

15 Cumulative Policies – Estimated Shortages of FTE RNs.............................19

16 LPN Staffing Intensity by Health Care Setting.............................................20

17 Factors Affecting LPN Staffing Intensity in Health Care Settings...............21

18 Estimated Nebraska Demand for FTE LPNs ................................................22

19 LPN FTE Supply in Year 2002.....................................................................23

20 Estimated Nebraska Supply of FTE LPNs 2006 – 2020...............................24

21 Estimated Surplus/Shortage of FTE LPNs ...................................................25

The Supply and Demand for Registered Nurses and Licensed Practical Nurses in Nebraska


Economic Consulting [email protected] 1

I Introduction

The analysis begins by examining nursing demand. Demand is modeled using the

National Center for Health Workforce Analysis’s Nursing Demand Model (NDM).

Demand is tied to population projections and the health care needs of individuals in

various age groups. The underlying data used in the NDM are adjusted to make them

more reflective of Nebraska’s experience. The model suggests that demand will grow

from a need for 16,000 full-time equivalent registered nurses (FTE RNs) in the year 2006

to over 20,000 by the year 2020.

Section three examines nursing supply using the National Center for Health Workforce

Analysis’s Nursing Supply Model (NSM). Supply is modeled as a function of the

changing population of nurses, their participation in the labor market, and their decisions

whether to work full or part time. The underlying data used in the NSM are also adjusted

to make them more reflective of Nebraska’s experience. The model suggests that the

supply of FTE RNs will grow from 15,300 in the year 2006 to 16,500 by the year 2020.

In section four, results from the two models are combined to look at projected nursing

shortages. The models suggest that by the year 2020, Nebraska will have a shortage of

about 3,800 FTE RNs, or about 20 percent of demand. Section five describes policies

that can be used to reduce the shortage. Policies are broken into two types: those that

increase the size of the labor pool by adding more nurses, and those that increase

participation among the existing pool of nurses.

Section six uses the NDM to project licensed practical nurses (LPN) demand between the

years 2006 and 2020. Demand grows by almost 30 percent over that period from 6,000

to 7,680 FTE LPNs. LPN supply is estimated in section seven. Nebraska survey results

are used to estimate the supply in 2002. Then, RN supply growth rates are used to

predict LPN supply between 2006 and 2020. Section eight shows that, as might be

expected, there is a shortage of LPNs each year, growing from eight percent of demand in

2006 to almost 23 percent of demand by 2020. While the data do not allow for scenario

analysis, it can reasonably be assumed that the same policies that will eliminate RN

shortages may reduce or eliminate the expected LPN shortages as well. The report ends

with a set of recommendations to address shortages in both labor markets.

II The Nursing Demand Model

The Nursing Demand Model (NDM) uses a three-step process to predict annual demand

for FTE nurses through the year 2020. In the first step it estimates the number of people

requiring care in twelve different health care settings. In the second step, the model

estimates the FTE RNs required per capita in each setting. The last step multiplies these

two figures to project FTE RN demand.

Number of People Requiring Care

U.S. Census Bureau population projections form the basis for estimating the number of

people requiring care in each health care setting. These projections are by age group, sex

and rural/urban setting. The NDM then converts population projections into numbers of




people needing care in each of the twelve health care settings. Historic health care

experience is used to determine the usage of health care facilities by people of different

ages, sexes and urban/rural settings. These figures are then adjusted for trends in the

health care market environment, economic conditions, demographics and geographic

location.

The factors affecting numbers of patients in each health care setting are shown in Table 1.

A negative sign (--) indicates that all else equal, an increase in that factor will decrease

the number of people using that health care environment. For example, an increase in the

percentage of the population in HMOs will decrease the number of people making in-

patient visits to hospitals. A positive sign (+) indicates that an increase in that factor will

increase the number of people using that health care environment, all else equal. For

example, an increase in the percentage of hospital surgeries that are performed as

outpatient surgeries will increase the number of people making out-patient visits to

hospitals.

Table 1

Factors Affecting Numbers of Patients in Health Care Settings Short-Term Hospital

In-

Patient

Out-

Patient

ER

Long-

Term

Hospital

Nursing

Facility

Home

Health

Health Care Environment

HMO Usage (--) (--) (--) (+)

Percent Hospital

Surgeries as Outpatient

Surgeries

(--) (+) (+)

Economic Conditions

Population Uninsured (--) (--)

Population Medicaid

Eligible (+) (+) (+) (+) (+)

Per Capita Income (+)

Demographics

Population Non-White (+) (+)

Population Hispanic (--)

Geographic Location

Percent Urban (--) (+) (--) (--)

West-North-Central (--)

Table 1 is important because it indicates the variables that can be influenced to affect

nursing demand. Increasing Medicaid eligibility, for example, increases the population

that uses a variety of health care facilities and, therefore, the demand for nursing in a

variety of settings. HMO usage rates, on the other hand, decrease nursing demand.




FTE RNs per Capita

The second step in estimating the demand for FTE RNs is to calculate the required FTE

RNs per capita in each health care setting, also referred to as staffing intensity. The nurse

staffing intensity measures are shown in Table 2, below. Typically staffing intensity is

measured either as FTE RNs required per 1,000 patient units or as FTE RNs per 10,000

in population. For nurse educators, staffing intensity is measured as a constant number of

educators per RN.

In the first seven settings shown in Table 2 (inpatient, outpatient and emergency care in a

short-term hospital; long-term hospital; nursing facility; home health; and physicians’

offices), nurse staffing intensity is determined by regressing historic staffing intensities

on factors reflecting the health care environment, economic conditions, health and acuity,

and geographic location. In the final five settings shown in Table 2 (occupational health,

schools, public health, other settings, nurse education) the ratio of FTE RNs per unit of

population is assumed to remain constant over time, based on 1996 usage patterns.

Table 2

RN Staffing Intensity by Health Care Setting

Setting RNs Short-term Hospitals

Inpatient RNs/1,000 inpatient days

Outpatient RNs/1,000 outpatient days

Emergency RNs/1,000 ER visits

Long-term/Psychiatric/Other Hospitals RNs/1,000 inpatient days

Nursing Facilities RNs/resident

Home Health RNs/1,000 Household visits

Physicians’ Office RNs/10,000 population

Occupational Health RNs/10,000 population age 18-64

Schools RNs/10,000 population age 5-17

Public Health RNs/10,000 population

All Other Settings RNs/10,000 population

Nurse Education RN educators/total RNs




For the seven health care settings in the top of Table 2, the factors affecting staffing

intensity are shown in Table 3, below. A negative sign (--) indicates that an increase in

that factor will decrease staffing intensity, all else equal. For example, an increase in the

ratio of RN to LPN wages will decrease the number of FTE RNs per 1,000 in-patient

visits to hospitals. A positive sign (+) indicates that an increase in that factor will increase

the staffing intensity, all else equal. For example, an increase in the Medicare payment

per home health visit will increase the number of FTE RNs per 1,000 home health visits.

Table 3

Factors Affecting RN Staffing Intensity in Health Care Settings Short-Term Hospital

In-

Patient

Out-

Patient

ER

Long-

Term

Hospital

Nursing

Facility

Home

Health

Physician

Offices

Health care Environment

RN/LPN Wage (--) (--) (--) (--)

HMO Usage (+) (+) (+) (--)

Medicare Payment/

Home Health Visit (+)

Medicaid Nursing

Facility Daily Rate (+)

Percent Hospital


Surgeries

(+)

Economic Conditions

Population Uninsured (--) (+) (--)

Population Medicaid

Eligible (--) (--)

Per Capita Income (+)

Health/Acuity

Mean Population Age (+)

Acuity (+)

Geographic Location

Percent Urban (+) (+) (--)

Table 3 is important because it indicates the variables that can be influenced to affect

staffing intensity and, consequently, nursing demand. Increasing the percentage of

hospital surgeries that are performed on an outpatient basis, for example, increases

staffing intensity and, therefore, the demand for nursing in outpatient settings. An

increase in RN wages relative to LPN wages, on the other hand, decreases nursing

demand.

It is important to note that the NDM uses relative wages to determine nursing demand.

For example, if wages for nurses, LPNs and nursing assistants all rise by ten percent,

there would be no impact on nursing demand. However, if nurse wages rose by a greater

percentage than LPN or aide wages, nursing demand would decline in some health care

settings as relatively less expensive LPNs and aids are substituted for nurses.




FTE Nursing Demand

The final step estimates FTE RN demand. The demand is calculated as the units of

health care usage in each setting multiplied by FTE RNs per unit of health care usage.

For example, if there are 2.6 million outpatient hospital visits estimated for a year and

there is one FTE RN per 1,000 visits, expected demand in that health care setting would

be 2,600 FTE RNs.

Making the Model Nebraska Specific

Most of the variables in the model reflected Nebraska’s nursing demand experience.

However, there were a few that required adjustment.

Population

The U.S. Census Bureau publishes population projections by year, age group and sex to

the year 2020. The age groups are 0-4, 5-17, 18-24, 35-44, 45-64, 65-74, 75-84, and 85+

years old. The Census projections were made in 1996 and again in 2003. The NDM

creators adjusted the 1996 Census projections so they pass through actual census

population counts for the year 2000.

The Census Bureau also publishes population estimates for the years 2000 through 2004.

The NDM projections are compared to Census population estimates for those four years.

Comparison shows that the NDM projections are, on average, about five percent too high

for men age 65-74 and about five percent too low for men age 85 and over. All other

NDM projections are consistent with Census data. The NDM projections for these two

groups are adjusted to reflect actual population experience in Nebraska.

RN Usage

The NDM predicts FTE RN usage from 1996 through 2020. In its default setting, the

model calibrates its predictions to actual nursing demand in the year 2000. Walburn and

Kelly have collected nursing demand data for Nebraska in the year 2002.1 Adjustments

are made to the NDM to calibrate to 2002 demand.

1 Walburn, Cheryl and Charlene Kelly, Nebraska RN Survey Report 2003, Nebraska Health and Human

Services, 2003.




The NDM assumes a nurse is one FTE equivalent if that nurse works 30 or more hours

per week. Nurses working anything less than 30 hours are treated as one half of an FTE.

Walburn and Kelly show the number of nurses and the number of hours they work

broken into ten-hour incremental blocks. According to their survey results,

approximately 77 percent of Nebraska’s nurses surveyed worked full time for one FTE

each and the remaining 23 percent worked part time for one half of one FTE each. These

figures are shown in Table 4, below.

Table 4

Adjusting the NDM to Reflect Nebraska FTE RNs in 2002

Walburn and Kelly show that in 2002 16,855 nurses worked in nursing positions that

required an RN license. Assuming that 77 percent of the 16,855 nurses worked full time

and the other 23 percent worked part time, this implies 12,949 working full time for one

FTE and another 3,906 working part time for ½ FTE each. Adding implies that Nebraska

had 14,902 FTE RN positions in 2002.

The NDM assumes that there was a shortage of nurses to work in hospital settings in the

late 1990s and into the 2000s. It assumes that actual demand for FTE RNs in hospital

settings was seven percent higher than observed employment. Data from the Walburn

and Kelly study show that about 55 percent of nurses were employed in hospitals.

Multiplying the 14,902 FTE RNs by 55 percent and adding seven percent estimates the

demand in hospitals to be about 8,818 FTE RNs . The remaining 45 percent represents

demand in non-hospital settings, or 6,661 FTE RNs. Adding demand in both settings

indicates that Nebraska’s actual 2002 demand was about 15,479 FTE RNS.

Percent of surveyed Nebraska nurses working full time (1 FTE) 77%

Percent of surveyed Nebraska nurses working part time (1/2 FTE) 23%

Nebraska nurses working in positions requiring an RN license in 2002 16,855

Full time RNs (1 FTE each) 12,949

Part time RNs (1/2 FTE each) 3,906

Implied Nebraska FTE RNs working in positions requiring an RN license in 2002 14,902

Percent of RNs working in hospital settings 55%

Percent of RNs working in non-hospital settings 45%

FTE RNs working in hospitals 8,241

Adjustment for demand shortage 7%

FTE RN demand from hospitals 8,818

FTE RN demand from non-hospital settings 6,661

Actual Nebraska 2002 FTE RN demand 15,479

NDM Nebraska 2002 FTE RN demand 15,200

Adjustment 1.84%




The NDM predicts Nebraska’s 2002 demand for FTE RNs as 15,200, an underestimate of

1.84 percent. The NDM is adjusted to account for this underestimate.

Results

Table 5 shows the NDM’s projections for FTE RN demand in Nebraska from 2006

through 2020. Projections increase from a demand of just over 16,000 FTE nurses in

2006 to just over 20,000 FTE RNs in 2020. This represents an increase in demand of

about 25 percent. This strong growth in the demand for FTE RNs is mainly a result of

the aging of Nebraska’s population and their relatively more care-intensive needs.

Table 5

NDM Estimated Nebraska Demand for FTE RNs

Year FTE RN Demand

2006 16,182

2007 16,428

2008 16,685

2009 16,909

2010 17,133

2011 17,379

2012 17,668

2013 17,947

2014 18,276

2015 18,567

2016 18,915

2017 19,225

2018 19,608

2019 19,935

2020 20,329

III The Nursing Supply Model

The Nursing Supply Model (NSM) uses a three-step process to predict the annual supply

of FTE nurses through the year 2020. In the first step, the process estimates the annual

potential RN population. The second step determines a participation rate and multiplies it

by the RN population to determine the RNs that are participating in the nursing labor

market. (Participation means that an RN is either employed or looking for employment.)

The third step develops a measure of FTE RN equivalents. The FTE RN equivalent

measure is multiplied by the number of RNs participating in the labor market to get the

FTE RN labor supply each year.

Many of the factors that influence the potential RN population, the participation rate and

the FTE equivalent rate are themselves dependent upon age and education level. Hence,

the NSM keeps track of the nursing population by age and by education level.




RN Population

The NSM begins by estimating the nursing population in each year. To do this, it starts

with the population from the previous year. It then determines the net migration of

nurses in to or out of the state, changes in education, attrition, foreign immigration and

new graduates into the nursing labor pool.

Starting Population Pool

The starting population is the number of registered nurses in the state in the year 2000.

This information is disaggregated by age and education.

Migration

To determine net migration, the model uses pre-estimated probabilities of immigration

into or emigration out of each state. The probabilities depend on an RN’s age and

education level. Older or more educated nurses tend to be more stable than younger or

less educated nurses. The actual number of immigrants into or emigrants out of a state in

a particular year is the nursing population times the probability of immigration or

emigration. Net migration is measured as the number of nurses entering the state minus

the number of nurses leaving the state.

Education

The model predicts many changes in labor market supply based on education. Hence, it

has to predict changes in education among the nursing pool. It assumes that all nurses

earning a bachelor’s degree are new entrants into the market. Only nurses earning

master’s degrees or higher are assumed to be current labor market participants upgrading

their education. To predict how many nurses will earn advanced degrees, the model

starts with pre-estimated probabilities of upgrading by current education and age. It

applies those probabilities to the nursing labor pool to estimate the number of nurses that

earn advanced degrees.

Attrition

Attrition is a permanent departure from the labor market. To estimate attrition, the model

uses pre-estimated probabilities that a nurse with a specified education and age will leave

the labor market. It applies the probabilities to the nursing labor pool to estimate the

number of nurses that actually exit each year.

Foreign Immigration

The model’s statistics show that there is almost no foreign immigration into the Nebraska

RN labor market. This potential source of RN supply is insignificant.

New Graduates

The NSM assumes that every one percent change in the pool of potential applicants for

nursing schools as compared to the pool that existed in the year 2000 results in a one

percent change in the number of nursing school graduates as compared to the number of

graduates in the year 2000. The pool of potential applicants to nursing schools is the

number of women age 20 to 44. For example, if the proportion that women age 20 to 44

make up of Nebraska’s population falls by two percent in the year 2010 as compared to




2000, the number of graduates from nursing programs will fall by two percent in 2010 as

compared to the number that graduated in 2000.

RN Participation

Participation measures the probability that a nurse will be either employed or looking for

work. The model uses pre-estimated national participation rates by age and education.

For example, there may be a 96 percent chance that a 30 year old with a Masters Degree

is participating in the RN labor market and only a 30 percent chance that a 64 year old

with a diploma is participating. The NSM applies the participation rates to the nursing

labor pool to estimate the number of nurses that actually participate in the labor market

each year.

RN FTE Equivalents

FTE equivalent rates estimate the proportion of nurses that work full time (for one FTE)

and the proportion that work part time (for ½ FTE). The NSM creators used national data

stratified by age and education to pre-estimate FTE RN equivalent rates. The model

multiplies the full time and part time proportions to the nursing labor pool to estimate the

supply of FTE RNs each year.

FTE Nursing Supply

The model multiplies the population of nurses by participation rates and FTE RN

equivalent rates to estimate the FTE RN supply in each year.

Making the Model Nebraska Specific

Most of the variables in the model reflected Nebraska’s nursing supply experience. Two,

however, required adjustment.

Initial Number of Nurses

Walburn and Kelly have collected nursing supply data for Nebraska in the year 2000.2

They estimate that there were 19,580 registered nurses in the state. The NSM shows

there were 18,571 registered nurses in the state in 2000. Hence, the NSM underestimates

by 5.4 percent. The year 2000 beginning nurse population is increased by 5.4 percent to

make the starting data more reflective of Nebraska.


Services, 2001.




Initial Number of Graduates

The nursing supply model uses an initial value of number of new combined Associate

and Diploma graduates, as well as Bachelors graduates. The first row of Table 6 shows

that the model assumed there were 259 Associate and Diplomas and 427 BSNs. The next

five rows show Nebraska’s actual graduation experience in the years 2000 through 2004.

The averages over that period are 294 and 487. These figures are substituted into the

model.

Table 6

Number of Nurse Graduates from Nebraska Colleges and Universities

Number of

Associate and

Diploma Degrees

Number of

Bachelor’s Degrees

Model Default 259 427

Nebraska Actuals

Year

2000 268 486

2001 260 443

2002 314 493

2003 300 463

2004 326 554

Average 294 487

The NSM uses the population of women aged 20 to 44 as the potential pool of applicants

to nursing schools. This clearly leaves men out of the market. However, the model uses

only relative changes in the population of women in this age group to predict changes in

nursing school enrollments. As long as the male population increases at the same relative

rate as the female population, there should be no inconsistency in estimating the nurse

population.




Results

Table 7 shows the NSM’s projections of the FTE RN supply in Nebraska from 2006

through 2020. Projections increase from a supply of 15,293 FTE nurses in 2006 to

16,491 FTE RNs in 2020. This represents an increase in supply of about eight percent

over the 15 year period.

Table 7

NSM Estimated Nebraska Supply of FTE RNs

Year FTE RN Supply

2006 15,293

2007 15,467

2008 15,620

2009 15,780

2010 15,917

2011 16,041

2012 16,134

2013 16,224

2014 16,296

2015 16,366

2016 16,406

2017 16,441

2018 16,451

2019 16,471

2020 16,491

Slow growth in the supply of FTE RNs occurs for several reasons. The main drivers,

however, are the aging of the RN supply and limited replacement with new nurses. In the

year 2000, a relatively large percentage of nurses were ages 40 to 49. This meant that a

significant segment of the RN population was staying in Nebraska and working full time.

By 2015, this large segment of the labor market will be in the 55-64 age bracket. Most of

these nurses will either work part time or leave the labor market altogether. This will

decrease the supply of nurses. Unfortunately, demographics play against replacing these

losses with large numbers of new nurses. As Nebraska’s population ages, there will be a

smaller pool of new graduates from nursing colleges. Younger nurses also tend to be

more migratory. The model’s underlying statistics show that Nebraska faces net out-

migration rates of about 100 FTE RNs per year. The cumulative effect is to make the

supply of FTE nurses rise by smaller and smaller amounts.




IV Comparing RN Supply and Demand

Table 8 shows the demand, supply and surplus/(shortage) of nurses in each year.

Shortages are shown in parentheses. The shortage grows from 889 FTE RNs in 2006 to

over 3,800 FTE RNs in 2020. In relative terms, the market goes from not filling about

six percent of demand to not meeting almost 20 percent of demand.

Table 8

Estimated Surplus/Shortage of FTE RNs

Year

FTE RN

Demand

FTE RN

Supply

Surplus/

(Shortage)

Relative

Surplus/Shortage

2006 16,182 15,293 (889) -5.5%

2007 16,428 15,467 (961) -5.8%

2008 16,685 15,620 (1,065) -6.4%

2009 16,909 15,780 (1,129) -6.7%

2010 17,133 15,917 (1,216) -7.1%

2011 17,379 16,041 (1,338) -7.7%

2012 17,668 16,134 (1,534) -8.7%

2013 17,947 16,224 (1,723) -9.6%

2014 18,276 16,296 (1,980) -10.8%

2015 18,567 16,366 (2,201) -11.9%

2016 18,915 16,406 (2,509) -13.3%

2017 19,225 16,441 (2,784) -14.5%

2018 19,608 16,451 (3,157) -16.1%

2019 19,935 16,471 (3,464) -17.4%

2020 20,329 16,491 (3,838) -18.9%

V Policies to Impact Shortages

Factors Influencing Demand

One way to reduce the shortage is to slow the rate of demand growth. The steady

increase in growth is fueled by Nebraska’s aging population and its relatively nursing

intensive health care needs. Clearly it is not possible to change the state’s demographic

trends. Instead, it may be possible to change the number of FTE nurses required to care

for an aging population.

Tables 1 and 3 in section II above suggest factors that will cut back on RN demand.

There may be other ways that technology or staffing changes can be used to reduce RN

demand.

Factors Influencing Supply

Several factors influence the supply of RNs. A number of major factors are addressed in

the following subsections.

Migration

According to the NSM, Nebraska loses about 100 RNs every year due to net out-

migration. Migration typically occurs among younger nurses. Hence policies that




discourage emigration and encourage immigration would help keep a larger and younger

RN workforce.

To explore the impact that migration policies may have, emigration rates are reduced ten

percent and immigration rates are increased ten percent. Table 9 shows the results. The

three columns under the heading “Baseline Model” repeat the demand, supply and

resulting shortage figures from the initial estimation. The next two columns under the

heading “Augmented Model” show the supply effects and resulting shortage with the

change in the supply model. Reducing emigration rates and increasing immigration rates

clearly increases the supply of FTN RNs and reduces the predicted shortage.

The last column in Table 9 shows the relative reduction in the shortage. Compared to the

baseline model, reducing emigration rates and increasing immigration rates reduces the

year 2020 shortage from 3,838 to 2,512 FTE RNs, or by about 35 percent. The benefit of

a policy to stimulate net in-migration is that its effects are cumulative over time. Nurses

that are attracted into Nebraska early in their careers tend to stay in the state throughout

their careers. In addition, more nurses either remain in Nebraska or join the labor supply

each year.

Table 9

Decreasing Emigration Rate and Increasing Immigration Rate --

Estimated Shortage of FTE RNs

Baseline Model Augmented Model

Year

FTE RN

Demand

FTE RN

Supply

Surplus/

(Shortage)

FTE RN

Supply

Surplus/

(Shortage)

Reduction in

Shortage

2006 16,182 15,293 (889) 15,391 (791) 11.0%

2007 16,428 15,467 (961) 15,663 (765) 20.3%

2008 16,685 15,620 (1,065) 15,912 (773) 27.4%

2009 16,909 15,780 (1,129) 16,167 (742) 34.3%

2010 17,133 15,917 (1,216) 16,398 (735) 39.6%

2011 17,379 16,041 (1,338) 16,613 (766) 42.8%

2012 17,668 16,134 (1,534) 16,797 (871) 43.2%

2013 17,947 16,224 (1,723) 16,977 (970) 43.7%

2014 18,276 16,296 (1,980) 17,103 (1,173) 40.8%

2015 18,567 16,366 (2,201) 17,293 (1,274) 42.1%

2016 18,915 16,406 (2,509) 17,416 (1,499) 40.3%

2017 19,225 16,441 (2,784) 17,533 (1,692) 39.2%

2018 19,608 16,451 (3,157) 17,624 (1,984) 37.1%

2019 19,935 16,471 (3,464) 17,721 (2,214) 36.1%

2020 20,329 16,491 (3,838) 17,817 (2,512) 34.5%

Education

The NSM’s background research shows that nurses with advanced degrees tend to stay in

the labor market longer and are more likely to work full time than their less educated

counterparts. The NSM assumes that the number of nurses upgrading their educations is

constant at the number that upgraded in the year 2000. These numbers are increased by




10 percent to examine the impact that a greater number of nurses upgrading their degrees

will have on future nursing shortages.

Results of the NSM show that upgrading education would have no impact on the

projected FTE RN shortage. This occurs for several reasons. The number of nurses that

upgrade is small. Nurses also generally upgrade at relative young ages. By the year

2020, most of the nurses with higher degrees are still in mid-career, a time when most

nurses are working full time. Hence their change in educational status has little impact

on the market as a whole.

Attrition

Attrition is the percentage of nurses that permanently leave the labor market at a given

age. While young nurses have almost no attrition, the second column of Table 10 shows

that the NSM default attrition rates for RNs 60 and older can be quite high. At ages 60

and 61, about five percent of RNs are leaving the work force each year. At ages 63 and

64, nearly seven percent of RNs are leaving the work force. Almost a quarter of nurses

ages 62 or 65 and older leave the market each year. These attrition rates imply that if

there are 100 nurses in the labor market starting at age 62, only about 50 will remain by

the time they reach 66.

Table 10

Attrition Rates for RNs by Age

Age

Nursing Supply Model

Default Percent of RNs

Leaving Work Force

First Alternative for

Percent of RNs

Leaving Workforce

Second Alternative

for Percent of RNs

Leaving Workforce

60 4.8% 4.8% 4.8%

61 4.9% 4.9% 4.9%

62 25.2% 13.4% 22.7%

63 6.9% 13.4% 6.2%

64 6.9% 13.4% 6.2%

65 23.2% 23.2% 20.9%

66 and older 25% 25% 25%

The NSM allows users to adjust attrition rates by age. The third column of Table 10

shows the first alternative measure of the percentage of RNs leaving the workforce. This

alternative smoothes the attrition rates for ages 62 through 64, yet maintains the result

that half of nurses that were in the market at age 62 would leave the market by age 66.




Table 11 shows the results of re-estimating the NSM after smoothing attrition rates. The

projected FTE RN shortage is reduced by approximately one percent in each year.

Attrition reduction policies will not be extremely successful because they impact only a

relatively small percentage of nurses that are near the end of their careers.

Table 11

Smoothing Attrition Rates Among Nurses Ages 62 – 64

– Estimated Shortages of FTE RNs


Year

FTE RN

Demand

FTE RN

Supply

Surplus/

(Shortage)

FTE RN

Supply

Surplus/

(Shortage)

Reduction in

Shortage

2006 16,182 15,293 (889) 15,303 (879) 1.2%

2007 16,428 15,467 (961) 15,484 (944) 1.7%

2008 16,685 15,620 (1,065) 15,637 (1,048) 1.6%

2009 16,909 15,780 (1,129) 15,795 (1,114) 1.3%

2010 17,133 15,917 (1,216) 15,935 (1,198) 1.5%

2011 17,379 16,041 (1,338) 16,061 (1,318) 1.5%

2012 17,668 16,134 (1,534) 16,158 (1,510) 1.6%

2013 17,947 16,224 (1,723) 16,249 (1,698) 1.5%

2014 18,276 16,296 (1,980) 16,292 (1,984) -0.2%

2015 18,567 16,366 (2,201) 16,396 (2,171) 1.4%

2016 18,915 16,406 (2,509) 16,440 (2,475) 1.4%

2017 19,225 16,441 (2,784) 16,477 (2,748) 1.3%

2018 19,608 16,451 (3,157) 16,490 (3,118) 1.2%

2019 19,935 16,471 (3,464) 16,512 (3,423) 1.2%

2020 20,329 16,491 (3,838) 16,532 (3,797) 1.1%




An alternative attrition assumption may be considered as well. The last column of Table

9, above, shows attrition rates if they are reduced by ten percent in each year for ages 62

through 65. The results of reducing attrition in such a manner are shown in Table 12,

below. Again, there is almost no impact on reducing the RN shortage. Reducing attrition

does not seem to be the answer.

Table 12

Reducing Attrition Rates Among Nurses Ages 62-65



Year

FTE RN

Demand

FTE RN

Supply

Surplus/

(Shortage)

FTE RN

Supply

Surplus/

(Shortage)

Reduction in

Shortage

2006 16,182 15,293 (889) 15,300 (882) 0.8%

2007 16,428 15,467 (961) 15,479 (949) 1.3%

2008 16,685 15,620 (1,065) 15,636 (1,049) 1.5%

2009 16,909 15,780 (1,129) 15,798 (1,111) 1.6%

2010 17,133 15,917 (1,216) 15,937 (1,196) 1.6%

2011 17,379 16,041 (1,338) 16,063 (1,316) 1.6%

2012 17,668 16,134 (1,534) 16,158 (1,510) 1.6%

2013 17,947 16,224 (1,723) 16,249 (1,698) 1.5%

2014 18,276 16,296 (1,980) 16,292 (1,984) -0.2%

2015 18,567 16,366 (2,201) 16,396 (2,171) 1.4%

2016 18,915 16,406 (2,509) 16,438 (2,477) 1.3%

2017 19,225 16,441 (2,784) 16,475 (2,750) 1.2%

2018 19,608 16,451 (3,157) 16,489 (3,119) 1.2%

2019 19,935 16,471 (3,464) 16,511 (3,424) 1.2%

2020 20,329 16,491 (3,838) 16,532 (3,797) 1.1%




New Graduates

To examine the impact that increases in nursing school graduates will have on the FTE

RN supply, the number of nurses that are expected to graduate in any year is increased by

10 percent. Since it will take time to educate these new nurses, it is assumed that the

increased supply of new graduates will not start into the market until the year 2010.

The impact of increasing nursing school graduates is shown in Table 13, below. As more

new nurses join the labor market, the shortage is slowly reduced. Eventually it is reduced

by about 14 percent.

Table 13

Increasing Nursing School Graduates



Year

FTE RN

Demand

FTE RN

Supply

Surplus/

(Shortage)

FTE RN

Supply

Surplus/

(Shortage)

Reduction in

Shortage

2006 16,182 15,293 (889) 15,293 (889) 0.0%

2007 16,428 15,467 (961) 15,467 (961) 0.0%

2008 16,685 15,620 (1,065) 15,620 (1,065) 0.0%

2009 16,909 15,780 (1,129) 15,780 (1,129) 0.0%

2010 17,133 15,917 (1,216) 15,917 (1,216) 0.0%

2011 17,379 16,041 (1,338) 16,100 (1,279) 4.4%

2012 17,668 16,134 (1,534) 16,251 (1,417) 7.6%

2013 17,947 16,224 (1,723) 16,399 (1,548) 10.1%

2014 18,276 16,296 (1,980) 16,494 (1,782) 10.0%

2015 18,567 16,366 (2,201) 16,650 (1,917) 12.9%

2016 18,915 16,406 (2,509) 16,744 (2,171) 13.5%

2017 19,225 16,441 (2,784) 16,832 (2,393) 14.0%

2018 19,608 16,451 (3,157) 16,895 (2,713) 14.1%

2019 19,935 16,471 (3,464) 16,966 (2,969) 14.3%

2020 20,329 16,491 (3,838) 17,037 (3,292) 14.2%




FTE Rate

The FTE rate measures the percentage of the RNs at any age that work full time as

opposed to part time. Each full-time RN is counted as one FTE. Each part-time RN is

counted as one half of an FTE. An increase in the FTE rate would increase the

percentage of nurses working full time and increase the supply of FTE RNs.

To examine the impact of getting a greater percentage of nurses to work full time, the

FTE rate at each age is increased by ten percent. At some ages, especially for younger

nurses, the FTE rate is nearly one in the NSM’s default setting. Therefore, as the FTE

rate is increased, it is constrained to be no higher than 100 percent.

Table 14 shows the results of increasing the FTE rate. It has a significant and immediate

impact on the FTE RN supply. Through 2012 the shortage is completely removed. But

since it is not a policy that cumulatively increases the labor pool, the size of the impact

remains relatively the same over time. As a consequence, by 2013, demand begins to

outpace supply and a shortage returns. Over time, as supply remains static and demand

grows, the shortage increases. By 2020, the reduction in the shortage is down to only 43

percent.

Table 14

Increasing FTE Rates



Year

FTE RN

Demand

FTE RN

Supply

Surplus/

(Shortage)

FTE RN

Supply

Surplus/

(Shortage)

Reduction in

Shortage

2006 16,182 15,293 (889) 16,811 629 170.8%

2007 16,428 15,467 (961) 17,002 574 159.7%

2008 16,685 15,620 (1,065) 17,171 486 145.6%

2009 16,909 15,780 (1,129) 17,347 438 138.8%

2010 17,133 15,917 (1,216) 17,498 365 130.0%

2011 17,379 16,041 (1,338) 17,634 255 119.1%

2012 17,668 16,134 (1,534) 17,736 68 104.4%

2013 17,947 16,224 (1,723) 17,835 (112) 93.5%

2014 18,276 16,296 (1,980) 17,880 (396) 80.0%

2015 18,567 16,366 (2,201) 17,992 (575) 73.9%

2016 18,915 16,406 (2,509) 18,035 (880) 64.9%

2017 19,225 16,441 (2,784) 18,074 (1,151) 58.7%

2018 19,608 16,451 (3,157) 18,085 (1,523) 51.8%

2019 19,935 16,471 (3,464) 18,107 (1,828) 47.2%

2020 20,329 16,491 (3,838) 18,129 (2,200) 42.7%




Cumulative Policies

Table 15 shows the cumulative effects of instituting all of the supply enhancing policies

previously discussed. This includes reducing net out-migration, delaying retirements,

having more nurses work full time, and attracting new nurses to the market. The

cumulative effects are substantial. The shortage is eliminated through the year 2020.

Table 15

Cumulative Policies



Year

FTE RN

Demand

FTE RN

Supply

Surplus/

(Shortage)

FTE RN

Supply

Surplus/

(Shortage)

Reduction in

Shortage

2006 16,182 15,293 (889) 17,449 1,267 242.5%

2007 16,428 15,467 (961) 17,740 1,312 236.5%

2008 16,685 15,620 (1,065) 18,007 1,322 224.1%

2009 16,909 15,780 (1,129) 18,281 1,372 221.5%

2010 17,133 15,917 (1,216) 18,528 1,395 214.7%

2011 17,379 16,041 (1,338) 18,824 1,445 208.0%

2012 17,668 16,134 (1,534) 19,083 1,415 192.3%

2013 17,947 16,224 (1,723) 19,338 1,391 180.7%

2014 18,276 16,296 (1,980) 19,534 1,258 163.5%

2015 18,567 16,366 (2,201) 19,802 1,235 156.1%

2016 18,915 16,406 (2,509) 19,995 1,080 143.0%

2017 19,225 16,441 (2,784) 20,181 956 134.3%

2018 19,608 16,451 (3,157) 20,335 727 123.0%

2019 19,935 16,471 (3,464) 20,502 567 116.4%

2020 20,329 16,491 (3,838) 20,660 331 108.6%

VI The LPN Demand Model

The Nursing Demand Model also projects the demand for licensed practical nurses

(LPNs). It uses a three-step process to predict annual demand for FTE LPNs through the

year 2020. In the first step the model estimates the number of people requiring care in

five different health care settings. In the second step, the model estimates the FTE LPNs

required per capita in each setting. The last step multiplies these two figures to project

FTE LPN demand.

Number of People Requiring Care

The model develops the number of people requiring care in five health care settings as a

function of demographics. The settings are short-term hospitals, long-term hospitals,

nursing care, home health care and other settings. Fewer settings are used than for

predicting RN demand. However, data from Walburn and Kelly indicate that two-thirds

of LPNs were employed in either long-term care facilities, hospitals, or

public/community/home health. These employment categories match well with the five




settings used in the model.3 Probably the only category missing from the demand model

that Walburn and Kelly show as significant is LPN employment in physicians’ offices.

This LPN demand is combined into the “All Other Settings” category.

As with the nursing demand model, U.S. Census Bureau population projections form the

basis for estimating the number of people requiring care in each health care setting.

These projections are by age group, sex and rural/urban setting. The model then converts

population projections into numbers of people needing care in each of the five health care

settings. Historic health care experience is used to determine the usage of health care

facilities by people of different ages, sexes and urban/rural settings. These figures are

then adjusted for trends in the health care market environment, economic conditions,

demographics and geographic location.

FTE LPNs per Capita

The second step in estimating the demand for FTE LPNs is to calculate the required FTE

LPNs per capita in each of the five health care setting. The LPN staffing intensity

measures are shown in Table 16, below. In the first four settings, LPN staffing intensity

is determined by regressing historic staffing intensities on factors reflecting the health

care environment, economic conditions, health and acuity, and geographic location. For

the category “All Other Settings” the ratio of FTE LPNs per unit of population is

assumed to remain constant over time, based on 1996 usage patterns.

Table 16

LPN Staffing Intensity by Health Care Setting

Setting LPNs Short-term Hospitals LPNs/1,000 inpatient days

Long-term/Psychiatric/Other Hospitals LPNs/1,000 inpatient days

Nursing Facilities LPNs/resident

Home Health LPNs/1,000 Household visits

All Other Settings LPNs/10,000 population

3 Walburn, Cheryl and Charlene Kelly, Nebraska LPN Survey Report 2004, Nebraska Health and Human

Services, 2004.




The factors affecting LPN staffing intensities in the first four settings are shown in Table

17, below. A positive sign (+) indicates that an increase in that factor will increase

staffing intensity, all else equal. For example, an increase in the ratio of RN to LPN

wages will increase the number of FTE LPNs per 1,000 in-patient and long-term hospital

visits. A negative sign (--) indicates that an increase in that factor will decrease the

staffing intensity, all else equal. For example, an increase in the ratio of LPN to nurse’s

aide wages will decrease LPN home health visits per 1,000 households.

Table 17

Factors Affecting LPN Staffing Intensity in Health Care Settings

Short-Term

Hospital

Long-Term

Hospital

Nursing

Facility

Home

Health

Health care Environment

RN/LPN Wage (+) (+) (+)

LPN/Aide Wage (--)

Medicare Payment/

Home Health Visit (+)

Medicaid Nursing

Facility Daily Rate (+)

Percent of Hospital


Surgeries

(+)

Health/Acuity

Mean Population Age (+)

Acuity (+)

Geographic Location

Percent Urban (+)




Results

The number of people requiring care in each setting is multiplied by the usage measures

to estimate overall demand for LPNs. Table 18 shows the model’s projections for FTE

LPN demand in Nebraska from 2006 through 2020. Projections increase from a demand

of just under 6,000 FTE LPNs in 2006 to 7,680 FTE LPNs in 2020. This represents an

increase in demand of about 28 percent. This strong growth in the demand for FTE LPNs

is mainly a result of the aging of Nebraska’s population and their relatively more care-

intensive needs.

Table 18

Estimated Nebraska Demand for FTE LPNs

Year FTE LPN Demand

2006 5,989

2007 6,090

2008 6,191

2009 6,289

2010 6,388

2011 6,482

2012 6,597

2013 6,715

2014 6,840

2015 6,963

2016 7,100

2017 7,233

2018 7,380

2019 7,519

2020 7,680




VII LPN Supply

Unfortunately, there is no LPN supply model that can be used to predict supply in

Nebraska. Therefore, a more rudimentary process will be used to estimate the supply of

LPNs. First, data from a Nebraska LPN survey will be used to estimate the supply of

LPNs in the year 2002. Then rates of change in RN supplies will be applied to that figure

to estimate LPN supply in the years 2006 through 2020.

Walburn and Kelly have collected LPN supply data for Nebraska.4 They show that in

2002 there were 5,943 LPNs licensed in the state. This figure is shown in Table 19

below. According to Walburn and Kelly’s survey results, 98.7 percent of licensed LPNs

were either working in a position that required a license or looking for an LPN position.

Multiplying these two figures, it implies there were 5,866 licensed LPNs in Nebraska in

2002. Walburn and Kelly show the number of LPNs and the number of hours they

worked broken into ten-hour incremental blocks. According to their survey results,

approximately 80 percent of Nebraska’s LPNs surveyed worked full time for one FTE

each and the remaining 20 percent worked part time for one half of one FTE each. These

figures are shown in Table 19 as well. Assuming that 80 percent of the 5,866 LPNs

would work full time and the other 20 percent would work part time, this implies a

supply of 5,262 LPNs in the state in 2002.

Table 19

LPN FTE Supply in Year 2002


Services, 2003.

Nebraska LPNs 5,943

Percent either Employed as LPN or Looking for LPN Position 98.7%

Nebraska LPNs Employed or Looking for Employment 5,866

Percent of surveyed Nebraska LPNs working full time (1 FTE) 79.4%

Percent of surveyed Nebraska LPNs working part time (1/2 FTE) 20.6%

Full time LPNs (1 FTE each) 4,657

Part time LPNs (1/2 FTE each) 1,208

Implied Nebraska Supply of FTE LPNs 5,262




The growth rate in the supply of RNs is used to estimate the growth in LPN supply. The

first three columns of Table 20 show the year, supply of RNs and growth rate in RN

supply. Between 2002 and 2006, supply grew by about 4.6 percent, or a little over one

percent annually. Between 2006 and 2020, the annual rate of growth ranged between

1.14 and 0.06 percent. Column four of Table 20 shows the LPN supply in the year 2002.

Multiplying the year 2002 supply by the growth rates gives the implied supplies shown in

the last column of the table. Supply increases from 5,506 LPNs in 2006 to 5,937 in 2020.

Table 20

Estimated Nebraska Supply of FTE LPNs 2006-2020

RN Growth Rate LPN Implied Future

Year Supply in RN Supply Supply LPN Supply

2002 14,616 5,262

2006 15,293 4.63% 5,506

2007 15,467 1.14% 5,568

2008 15,620 0.99% 5,623

2009 15,780 1.02% 5,681

2010 15,917 0.87% 5,730

2011 16,041 0.78% 5,775

2012 16,134 0.58% 5,809

2013 16,224 0.56% 5,841

2014 16,296 0.44% 5,867

2015 16,366 0.43% 5,892

2016 16,406 0.24% 5,906

2017 16,441 0.21% 5,919

2018 16,451 0.06% 5,923

2019 16,471 0.12% 5,930

2020 16,491 0.12% 5,937




IV Comparing LPN Supply and Demand

Table 21 shows the demand, supply and surplus/(shortage) of LPNs in the years 2006

through 2020. Shortages are shown in parentheses. The shortage grows from 483 FTE

LPNs in 2006 to over 1,700 FTE LPNs in 2020. In relative terms, the market goes from

not filling about eight percent of demand to not meeting 23 percent of demand.

Table 21

Estimated Surplus/Shortage of FTE LPNs

Year

FTE LPN

Demand

FTE LPN

Supply

Surplus/

(Shortage)

Relative

Surplus/Shortage

2006 5,989 5,506 (483) -8.07%

2007 6,090 5,568 (522) -8.57%

2008 6,191 5,623 (568) -9.17%

2009 6,289 5,681 (608) -9.67%

2010 6,388 5,730 (658) -10.29%

2011 6,482 5,775 (707) -10.91%

2012 6,597 5,809 (788) -11.95%

2013 6,715 5,841 (874) -13.02%

2014 6,840 5,867 (973) -14.23%

2015 6,963 5,892 (1,071) -15.38%

2016 7,100 5,906 (1,194) -16.81%

2017 7,233 5,919 (1,314) -18.17%

2018 7,380 5,923 (1,457) -19.75%

2019 7,519 5,930 (1,589) -21.14%

2020 7,680 5,937 (1,743) -22.69%

One way to reduce the shortage is to slow the rate of demand growth. The steady

increase in growth is fueled by Nebraska’s aging population and their relatively nursing

intensive health care needs. Table 17 in section VI above suggests factors that will cut

back on LPN demand. There may be other ways that technology or staffing changes can

be used to reduce RN demand.

With no LPN supply model, it would be speculative to try and predict the numerical

impacts that changes in LPN retention policies would have on shortages. However, it

seems reasonable to expect that the policies that would reduce nursing shortages would

also reduce LPN shortages. These include policies to stem net out-migration, delay

retirements, reduce career changes, promote full time employment, and attract new LPN

graduates.




VII Recommendations

Several actions can be taken to prevent projected shortages in FTE RNs and LPNs over

the next 15 years. Near-term shortages can be reduced or eliminated if greater

participation can be coaxed from the existing supply of RNs and LPNs. This means

delaying retirements, keeping more RNs and LPNs in the profession and convincing a

greater percentage to work full time rather than part time.

At the same time, the stock of RNs and LPNs has to be increased. Since this will likely

be a slow building process, it should start soon to meet projected shortages into the next

decade. The stock can also be increased by reversing net out-migration trends, by getting

more applicants into schools, and by increasing graduation rates.

Care should be taken in adjusting wages in these two markets. The markets are

connected by relative wages. Increasing wages for RNs, for example, will increase the

supply of RNs and likely decrease the supply of LPNs as current and future LPNs opt

into the RN market. It will also decrease the demand for RNs and increase the demand

for LPNs. The overall impact will be to help alleviate the RN shortage but exacerbate the

shortage in the LPN market. A more reasonable policy may be to raise wages for RNs,

LPNs and Aides simultaneously so as not to disrupt relative wages across the markets.

the supply and demand for licensed practical nurses and ... · registered nurses and licensed...

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